From Bottleneck to Breakthrough

Facing performance bottlenecks, rising costs, and system complexity, our team modernized a fragmented architecture with a structured Proof of Concept, strategic development, and an Interoperability Layer to deliver a scalable, cost-efficient, high-performance solution.

Challange

Escaping the Limitations of Base Mirror Architecture

In modern cloud-native development, businesses demand fast, scalable, and cost-effective architectures. Yet, many companies still rely on Base Mirror architectures, built on Azure Logic Apps, which were once seen as a flexible, low-code integration solution. Over time, however, these systems failed to scale, became expensive, and introduced inefficiencies that hindered business growth.

Base Mirror Architecture

One of our clients—a rapidly expanding enterprise—struggled with performance bottlenecks, high costs, and operational inefficiencies due to their existing Base Mirror approach. Their system was plagued with issues such as:

1️⃣ Scalability & Performance Bottlenecks

The system relied on Logic Apps calling other Logic Apps, creating a tangled web of API calls.
Each request passed through multiple layers of orchestration, leading to latencies exceeding 5 seconds per request.
As the business grew, the architecture struggled to handle the increasing number of transactions, causing system slowdowns.

2️⃣ Unsustainable Costs

Logic Apps charge per execution and per connector, leading to exponentially increasing costs.
Since multiple Logic Apps were interconnected, a single process triggered multiple costly executions.
The more transactions the business handled, the higher the Azure bill, making long-term growth financially unviable.

3️⃣ Debugging & Maintainability Nightmares

Debugging issues required tracing logs across multiple Logic Apps, making troubleshooting slow and painful.
Logs were spread across layers, making it difficult to pinpoint the root cause of failures.
Each change to the workflow risked breaking other dependencies, increasing maintenance overhead.

Key Challenges We Overcame

✓Slow response times and system slowdowns due to inefficient orchestration.
✓Exponentially increasing Azure Logic App execution costs made long-term growth unviable.
✓Troubleshooting issues was time-consuming due to fragmented logs and interdependent workflows.

Solution

Breaking Free with Base Function Architecture

Realizing the inefficiencies of the Base Mirror system, we led a transformation to a Base Function architecture, powered by Azure Functions, event-driven processing, and a persistence layer. This shift brought massive improvements in performance, cost reduction, and maintainability.

1️⃣ Event-Driven, Serverless Execution

We replaced Logic App workflows with an event-driven system using Azure Functions, delivering:

Ultra-fast execution: Functions process requests in milliseconds, removing delays.
Direct processing: Requests are processed immediately without multiple layers.
Auto-scaling: Functions scale dynamically with traffic to prevent bottlenecks.

Cloud Events & AsyncAPI integration:

Strong validation: Ensured robust architecture with Cloud Events and AsyncAPI for validation.
Easy failure detection: Cloud Events capture metadata for quick failure identification.
Event monitoring: Real-time event flow visibility and custom dashboards for tracking progress, success rates, and metrics.
Event tracking: Full event lifecycle tracking for diagnostics and troubleshooting.

2️⃣ Smart Caching with a Persistence Layer

A major flaw in the previous system was that every request triggered an API call, even when data was unchanged. To fix this, we:

Implemented Azure Cosmos DB and Redis Cache as a smart persistence layer.
Stored frequently accessed data to reduce unnecessary API calls by over 80%.
Improved response times by serving cached data instantly instead of always querying external systems.

3️⃣ Enhanced Observability & Debugging

Debugging and monitoring were revolutionized by:

Centralized logging with Datadog and Sentry, allowing faster identification of issues.
Automated queue restoration, ensuring messages weren't lost due to failures.
Real-time monitoring dashboards, providing instant insights into system health.

4️⃣ Performance Overview & System Testing

Peformance Overview Study

To ensure optimal performance, we conducted extensive benchmarking tests. Our approach involved:

Load testing under real-world conditions to measure response times and throughput, ensuring the system could handle varying traffic loads efficiently.
Profiling different architectures to determine the most efficient setup for scalability, considering factors like cost, speed, and resource usage.
Custom performance evaluation services to help businesses identify and deploy the best architecture for their needs, ensuring that their systems can handle peak loads with minimal latency.
Identifying the best approach for business systems: Through a combination of load testing and profiling, we pinpointed the ideal architecture that aligned with the business's goals and operational requirements. By optimizing key components of the system, we achieved a 90% performance boost, significantly improving response times and throughput, reducing operational costs, and enhancing the overall user experience.

5️⃣ Automated End-to-End Testing for Stability

Previously, the system lacked automated regression testing, making it fragile during updates. We integrated modern E2E testing technologies, enabling:

Automated validation of business processes before deployment.
Continuous stability checks to prevent regressions with each release.
Scalable test execution using cloud-based testing platforms, ensuring high reliability across environments.

6️⃣ Seamless Interoperability: Unlocking the Full Potential of Base Functions

With an event-driven architecture, optimized caching, and enhanced observability, we introduced an Interoperability Layer—a centralized hub that orchestrates communication between Order Management Systems (OMS), Product Suppliers, APIs, monitoring tools, and frontend consumers.

By leveraging Base Functions, interoperability is no longer an afterthought—it is an integral part of a high-performance, scalable ecosystem.

Decoupled Microservices: Each module operates independently, reducing failure risks and improving maintainability.
Seamless System Expansion: Adding a new OMS, supplier, or API is as simple as plugging it into the Interoperability Layer—without requiring major changes to the existing system.
Optimized API Calls & Caching: The system intelligently determines when to call an external service versus retrieving cached data, preventing redundant API executions and optimizing performance.

The complexity of achieving such an architecture using a Base Mirror approach would be overwhelming. Logic Apps would introduce unnecessary latency, dependencies, and excessive API calls, leading to an unmanageable and expensive system. With Base Functions, we eliminated these inefficiencies, creating a robust and scalable ecosystem.

Azure

GitLab

Terraform

NestJS

Datadog

Event Architecture

Performance

Microservices

AsyncAPI

Success

From Failure to Launch: Transforming a Stalled Project into a Scalable Solution

Before we took over, the project had been in development for two years without reaching production. The previous team, relying on a low-code, workflow-heavy architecture, struggled with excessive costs, poor scalability, and slow performance. The system was too complex to debug, making deployments risky and unreliable. Despite significant investment, they were unable to launch.

When we stepped in, we completely reengineered the system, adopting a high-performance, event-driven Base Function architecture. In just a few months, we rebuilt the entire solution, rigorously tested it, and successfully deployed it to production.

The transformation led to game-changing improvements:

🚀 Performance Gains

API response times dropped from 5 seconds to under 300ms.
Real-time transactions enabled a seamless user experience.

💰 Cost Reductions

70% lower operational costs by eliminating redundant executions and API calls.
Replacing Azure Logic Apps significantly reduced execution costs.

🔧 Better Maintainability & Scalability

Debugging time reduced by 60% with centralized logging.
The system now scales effortlessly, handling increasing traffic without performance drops.

What once seemed impossible—going live after years of struggle—became a reality within months, proving the power of the right architecture and technical leadership.

💡 Is your business still relying on an outdated Base Mirror system? It’s time to transform your architecture and unlock peak performance.